PM Skills is a collection of plain-Markdown instructions that teach AI assistants structured methods for handling professional, personal, and life-admin tasks. People use it with Claude, ChatGPT, Gemini, Cursor, Codex, and other supported agents for work such as writing product requirements, reviewing documents, or planning difficult situations.
Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skillsnpx agentmods add rules/mohitagw15856/pm-claude-skills/rent-vs-buyWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/rules/mohitagw15856/pm-claude-skills/rent-vs-buy)<a href="https://agentmods.dev/rules/mohitagw15856/pm-claude-skills/rent-vs-buy"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/rent-vs-buy/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/rules/mohitagw15856/pm-claude-skills/rent-vs-buy"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/rent-vs-buy.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00099 | $0.01067 |
| Opus 5 | $0.00049 | $0.00534 |
| Sonnet 5 | $0.00020 | $0.00213 |
| Haiku 4.5 | $0.00010 | $0.00107 |
Grade A, and why
rent-vs-buy scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Rent vs Buy Skill
Rent-vs-buy arguments are usually two people comparing different questions: one counts equity and forgets transaction costs and carry; the other counts rent as "thrown away" and forgets the renter can invest the difference. This skill runs the symmetric model — both paths get their real costs and their real compounding — and delivers a breakeven horizon, because the honest answer is almost always "it depends how long you stay."
What This Skill Produces
- The year-by-year table — owner net position (equity minus selling costs) vs renter net position (invested savings), per year
- The breakeven year — before it, renting won; after it, buying won, on the stated assumptions
- The assumption ledger — every input labeled, defaults flagged as defaults
- The not-modeled list — taxes/deductions, renovation risk, the non-financials — stated up front
Required Inputs
Ask for these if not provided:
- Home price and comparable monthly rent — same home, same neighborhood; comparing a condo rent to a house purchase is the classic apples-to-oranges error
- Down payment %, mortgage rate, term (defaults 20% / 6.5% / 30yr, labeled)
- How long they expect to stay — the single most decision-relevant input
- Growth assumptions — appreciation, rent growth, investment return (defaults 3/3/5%, labeled)
Programmatic Helper
python3 scripts/rent_vs_buy.py --price 450000 --rent 2200
python3 scripts/rent_vs_buy.py --price 450000 --rent 2200 --horizon 10 --appreciation 2 --json
Deterministic. The renter's pot starts at the down payment + closing costs (the money a buyer parts with on day one) and each year absorbs the difference between owner outflow and rent. Selling costs are applied at every horizon — equity you can't access without paying 7% isn't fully yours.
Framework: The Symmetry Rules
- The renter invests the difference — the model's load-bearing assumption; a renter who spends the difference makes buying win almost automatically, and that's a behavior question, not a math question. Say so.
- Carry costs are real — tax, insurance, maintenance (~2%/yr of value) never build equity; "my mortgage is like rent" omits them
- Transaction costs decide short horizons — ~3% in and ~7% out is why breakeven is measured in years, not months
- Appreciation is an assumption, not a birthright — vary it before trusting a conclusion; a 1-point change often moves breakeven by years
- The output is a horizon, not a verdict — "buying wins if you stay past year N" is the honest deliverable
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 77 lines · 99 tokens per session scan A 2aae1854c830
rent-vs-buy is a cursor rule published in the GitHub repository mohitagw15856/pm-claude-skills (1,357 stars, last pushed yesterday), licensed MIT. It adds 99 tokens to every session and 1,067 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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